Generate and manipulate faces with StyleGANEX
Swap faces in videos or images
Find and highlight face landmarks in images
Identify emotions and sentiments from faces in images
happy or some other emotion - facial expression
emotion recognition
face parsing
Identify and mark facial landmarks in images
Recognize facial expressions from images
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Detect and visualize facial landmarks from a live video feed
Next generation image and video face swapper
Identify ethnicity group from a picture
StyleGANEX is a state-of-the-art tool designed for generating and manipulating high-quality faces using advanced generative adversarial networks (GANs). It builds upon the foundation of StyleGAN and introduces Cooperative GANs of Contra and StyleSpace for improved results. StyleGANEX is primarily used in face recognition and generation tasks, enabling users to create realistic and diverse facial images.
• Multi-Domain Support: Generate faces across multiple domains, including different ethnicities, ages, and lighting conditions.
• StyleSpace Control: Fine-tune generated faces using a robust style space for precise control over facial features.
• High-Resolution Images: Produce high-quality, realistic images with exceptional detail.
• Interpretable Edits: Make meaningful edits to generated faces using intuitive controls.
• Flexible Customization: Adjust various parameters to tailor outputs to specific needs.
pip install styleganex to install the package.import styleganex in your Python script to access the tool.model = StyleGANEX().model.generate() to create new faces. You can customize outputs by passing specific parameters (e.g., seed, style, etc.).What makes StyleGANEX different from other GANs?
StyleGANEX stands out due to its StyleSpace framework, which allows for precise control over facial features, enabling more interpretable and customizable generation.
Can I use StyleGANEX for non-face generation tasks?
While StyleGANEX is primarily designed for face generation, it can be adapted for other tasks with proper fine-tuning and domain-specific training.
How can I evaluate the quality of generated faces?
Use metrics like FID (Frechet Inception Distance) or IS (Inception Score) to evaluate the quality and diversity of generated faces.